{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "import matplotlib.lines as mlines\n",
    "from matplotlib.patches import ConnectionPatch\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pop_df=pd.read_csv(r'C:\\Users\\Yasaman\\Downloads\\World_bank_population.csv',skiprows=3)\n",
    "pop_df=pop_df[['Country Code','2019']].dropna()\n",
    "pop_df['2019']=pop_df['2019'].astype(int)\n",
    "possible_countries=pop_df.query(\" `2019` >=1000000\")['Country Code'].values\n",
    "possible_countries=[x.lower() for x in possible_countries]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(r\"C:\\Users\\Yasaman\\Downloads\\Attention-fractional counting.csv\")\n",
    "df=df[df['country'].isin(possible_countries)]\n",
    "df=df.rename(columns={'aggregated_value':'count', 'country':'Mention_country', 'affiliation_country':'Aff_country'})\n",
    "\n",
    "df=df[df['year'].isin(np.arange(2002, 2020, 1))]\n",
    "Country_list={'Egypt':'EGY', 'Tunisia':'TUN','Libya':'LBY','Syria':'SYR','Yemen':'YEM','Bahrain':'BHR','Jordan':'JOR','Kuwait':'KWT','Morocco':'MAR','Oman':'OMN'}\n",
    "rev_Country_list={Country_list[key]: key for key in Country_list}\n",
    "abbr=[country.lower() for country in Country_list.values()]\n",
    "physical_sciences=[ 'MATH', 'ENGI', 'PHYS', 'COMP','MULT']\n",
    "df=df[~df['subjarea'].isin(physical_sciences)]\n",
    "df['year_arabspring']=df['year'].apply(lambda x: 'before' if x>=2002 and x<=2010 else 'after' )\n",
    "df['is_internal_attention']=df['Mention_country']==df['Aff_country']\n",
    "result_df=df.groupby(['Mention_country', 'year_arabspring', 'is_internal_attention'])['count'].sum().reset_index()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Yasaman\\AppData\\Local\\Temp\\ipykernel_69048\\1104022864.py:4: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  .apply(lambda x: x.loc[x[\"is_internal_attention\"] == False, \"count\"].sum() / x.loc[x[\"is_internal_attention\"] == True, \"count\"].sum())\n"
     ]
    }
   ],
   "source": [
    "# Group by `cat` and `Mention_country` and compute the ratio of `count` for `is_internal_attention=False` to the total `count` for each group\n",
    "df_grouped = (\n",
    "    result_df.groupby([ \"Mention_country\", \"year_arabspring\"])\n",
    "    .apply(lambda x: x.loc[x[\"is_internal_attention\"] == False, \"count\"].sum() / x.loc[x[\"is_internal_attention\"] == True, \"count\"].sum())\n",
    "    .reset_index(name=\"Foriegn2Internal_attention_ratio\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Yasaman\\AppData\\Local\\Temp\\ipykernel_69048\\4138976991.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  subset['Mention_country'] = subset['Mention_country'].map(rev_Country_list)\n",
      "C:\\Users\\Yasaman\\AppData\\Local\\Temp\\ipykernel_69048\\4138976991.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  subset['sorted_order'] = subset['Mention_country'].apply(lambda x: country_names.index(x))\n",
      "C:\\Users\\Yasaman\\AppData\\Local\\Temp\\ipykernel_69048\\4138976991.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  subset['Mention_country'] = subset['Mention_country'].map(rev_Country_list)\n",
      "C:\\Users\\Yasaman\\AppData\\Local\\Temp\\ipykernel_69048\\4138976991.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  subset['sorted_order'] = subset['Mention_country'].apply(lambda x: country_names.index(x))\n",
      "C:\\Users\\Yasaman\\AppData\\Local\\Temp\\ipykernel_69048\\4138976991.py:69: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  subset['Mention_country'] = subset['Mention_country'].map(rev_Country_list)\n",
      "C:\\Users\\Yasaman\\AppData\\Local\\Temp\\ipykernel_69048\\4138976991.py:70: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  subset['sorted_order'] = subset['Mention_country'].apply(lambda x: country_names.index(x))\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 16500x6000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add brackets and messages\n",
    "def add_bracket(axis, x_start, x_end, y, text, ratio=1):\n",
    "    \"\"\"Add a bracket with text between two x positions.\"\"\"\n",
    "    axis.plot([x_start, x_start, x_end, x_end], [y, y + 1*ratio, y + 1*ratio, y], color=\"black\", lw=1.5)\n",
    "    axis.text((x_start + x_end) / 2, y +2.5*ratio, text, ha='center', va='bottom', fontsize=10)\n",
    "\n",
    "Country_list = {'Egypt':'EGY', 'Tunisia':'TUN', 'Libya':'LBY', 'Syria':'SYR', 'Yemen':'YEM',\n",
    "                'Bahrain':'BHR', 'Jordan':'JOR', 'Kuwait':'KWT', 'Morocco':'MAR', 'Oman':'OMN'}\n",
    "rev_Country_list = {Country_list[key].lower(): key for key in Country_list}\n",
    "country_names = list(Country_list.keys())\n",
    "custom_colors = [f'C{i}' for i in range(10)]\n",
    "fig, ax=plt.subplots(nrows=1,ncols=3, figsize=(11, 4), dpi=1500)\n",
    "Titles=['Domestic', 'Foreign']\n",
    "subplot_labels = ['A', 'B', 'C', 'D']\n",
    "\n",
    "for i, is_internal in enumerate([True, False]):\n",
    "    subset = result_df.query(f'is_internal_attention == {is_internal} and Mention_country in @abbr')\n",
    "    subset['Mention_country'] = subset['Mention_country'].map(rev_Country_list)\n",
    "    subset['sorted_order'] = subset['Mention_country'].apply(lambda x: country_names.index(x))\n",
    "    subset = subset.sort_values('sorted_order').drop(columns=['sorted_order'])\n",
    "\n",
    "    # Reset the index if needed\n",
    "    subset.reset_index(drop=True, inplace=True)\n",
    "    x_labels = subset['Mention_country'].unique()\n",
    "    x = np.array([0,1,3,4,5,7, 8, 9, 10, 11] ) # X positions for the bars\n",
    "    subset['count']/=9\n",
    "    width = 0.4  # Width of each bar\n",
    "    before_data = subset[subset['year_arabspring'] == 'before']['count']\n",
    "    after_data = subset[subset['year_arabspring'] == 'after']['count']\n",
    "    \n",
    "    ax[i].bar(\n",
    "        x - width / 2, \n",
    "        before_data, \n",
    "        width=width, \n",
    "        color=custom_colors, \n",
    "        label='Before'\n",
    "    )\n",
    "    \n",
    "    # Bars for 'after' with dashed patterns\n",
    "    ax[i].bar(\n",
    "        x + width / 2, \n",
    "        after_data, \n",
    "        width=width, \n",
    "        color='none', \n",
    "        edgecolor=custom_colors, \n",
    "        linewidth=1.5, \n",
    "        hatch='//', \n",
    "        label='After'\n",
    "    )\n",
    "    medians=result_df.query(f'is_internal_attention == {is_internal}').groupby('year_arabspring')['count'].median().to_dict()\n",
    "\n",
    "    ax[i].axhline(medians['before']/10, color='k')\n",
    "    ax[i].axhline(medians['after']/10, color='k', linestyle='--')\n",
    "    \n",
    "    # Set x-axis labels and title\n",
    "    ax[i].set_xticks(x)\n",
    "    ax[i].set_xticklabels(x_labels, rotation=90)\n",
    "    ax[i].set_title(Titles[i])\n",
    "    \n",
    "    # Add subplot label (A, B, C, D) to the top-left corner\n",
    "    ax[i].text(-0.1, 1.05, subplot_labels[i], transform=ax[i].transAxes, fontsize=15, fontweight='bold', va='center', ha='center')\n",
    "\n",
    "ax[0].set_ylabel('Average Scholarly attention')\n",
    "    \n",
    "ax[1].set_ylabel('Average Scholarly attention')\n",
    "\n",
    "\n",
    "subset = df_grouped.query(f'Mention_country in @abbr')\n",
    "subset['Mention_country'] = subset['Mention_country'].map(rev_Country_list)\n",
    "subset['sorted_order'] = subset['Mention_country'].apply(lambda x: country_names.index(x))\n",
    "subset = subset.sort_values('sorted_order').drop(columns=['sorted_order'])\n",
    "\n",
    "# Reset the index if needed\n",
    "subset.reset_index(drop=True, inplace=True)\n",
    "x_labels = subset['Mention_country'].unique()\n",
    "x = np.array([0,1,3,4,5,7, 8, 9, 10, 11] ) # X positions for the bars\n",
    "width = 0.4  # Width of each bar\n",
    "before_data = subset[subset['year_arabspring'] == 'before']['Foriegn2Internal_attention_ratio']\n",
    "after_data = subset[subset['year_arabspring'] == 'after']['Foriegn2Internal_attention_ratio']\n",
    "\n",
    "ax[2].bar(\n",
    "    x - width / 2, \n",
    "    before_data, \n",
    "    width=width, \n",
    "    color=custom_colors, \n",
    "    label='Before'\n",
    ")\n",
    "\n",
    "# Bars for 'after' with dashed patterns\n",
    "ax[2].bar(\n",
    "    x + width / 2, \n",
    "    after_data, \n",
    "    width=width, \n",
    "    color='none', \n",
    "    edgecolor=custom_colors, \n",
    "    linewidth=1.5, \n",
    "    hatch='//', \n",
    "    label='After'\n",
    ")\n",
    "\n",
    "ax[2].axhline(1, color='magenta', linestyle=':')\n",
    "\n",
    "# Set x-axis labels and title\n",
    "ax[2].set_xticks(x)\n",
    "ax[2].set_xticklabels(x_labels, rotation=90)\n",
    "#ax[i].set_ylim(0, 3500)\n",
    "\n",
    "# Add subplot label (A, B, C, D) to the top-left corner\n",
    "ax[2].text(-0.1, 1.05, 'C', transform=ax[2].transAxes, fontsize=15, fontweight='bold', va='center', ha='center')\n",
    "ax[2].set_ylabel('Foreign/Domestic attention ratio')\n",
    "    \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Create custom legend handles for the boxes\n",
    "solid_box = mpatches.Patch(facecolor='k', edgecolor='k', label='Before Arabspring')  # Solid box\n",
    "dashed_box = mpatches.Patch(facecolor='white', edgecolor='k', hatch='////', label='After Arabspring')  # Dashed pattern inside\n",
    "    # Create custom legend handles with grey color\n",
    "before_handle = mlines.Line2D([], [], color='k', label='Before world Median')\n",
    "after_handle = mlines.Line2D([], [], color='k', label='After world Median', linestyle='--')\n",
    "\n",
    "    # Add the legend with custom handles\n",
    "lgd=fig.legend(handles=[solid_box, dashed_box, before_handle,after_handle], loc='lower center', frameon=False, ncol=4,bbox_to_anchor=(0.5, -0.05))\n",
    "\n",
    "# Adding brackets for each group\n",
    "add_bracket(ax[0], -0.5, 2, 1100, \"GO\")\n",
    "add_bracket(ax[0], 2.5, 6.5, 1100, \"CW\")\n",
    "add_bracket(ax[0], 7, 11.5, 1100, \"GC\")\n",
    "ax[0].set_ylim(0, 1200)\n",
    "\n",
    "\n",
    "# Adding brackets for each group\n",
    "add_bracket(ax[1], -0.5, 2, 580, \"GO\")\n",
    "add_bracket(ax[1], 2.5, 6.5, 580, \"CW\")\n",
    "add_bracket(ax[1], 7, 11.5, 580, \"GC\")\n",
    "ax[1].set_ylim(0, 630)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "add_bracket(ax[2], -0.5, 2, 4.3, \"GO\",0.01)\n",
    "add_bracket(ax[2], 2.5, 6.5, 4.3, \"CW\",0.01)\n",
    "add_bracket(ax[2], 7, 11.5, 4.3, \"GC\",0.01)\n",
    "ax[2].set_ylim(0, 4.6)\n",
    "plt.tight_layout()\n",
    "fig.savefig('Figure2.pdf', bbox_extra_artists=(lgd,), bbox_inches='tight')\n",
    "plt.show()"
   ]
  }
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